Perceptual and Computational Task-Based Quality Assessment of Driving Video

基于感知和计算任务的驾驶视频质量评估

• 批准号: 22K12085
• 负责人: Chandler Damon
• 金额: $ 2.66万
• 依托单位: Ritsumeikan University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K12085/
• 关键词: quality assessment; driving video; visual perception; detection; AI

In this research, we ask: Is it possible to train a computer to automatically assess the quality of driving video? Specifically, we aim to better understand what signal and perceptual features the human visual system uses when judging driving video quality, and how these aspects can be modeled computationally.Two main objectives were proposed for 2022: (1) Create a driving video database, and (2) measure visibility thresholds. Both of these objectives have largely been accomplished; however, follow-up research and additional data collection is necessary. For (1) we have collected driving videos for a wide variety of environments, including nighttime videos and scenes that contain glare. We have also captured videos of outdoor sidewalk scenes. For (2) we have measured visibility thresholds for JPEG artifacts added to the periphery of each video when subjects were forced to fixate on the center of the video (the free-viewing measurements are still ongoing).Several student thesis projects related to this research were completed/continued in 2022. One MS student performed glare detection in the driving videos. One MS student performed de-nighting of nighttime driving videos. These MS projects gave rise to conference publications. BS student projects' themes included roadway detection, detection of words painted on roads, and detection of roadside electric/light poles.

在这项研究中，我们问：有没有可能训练计算机自动评估驾驶视频的质量？具体而言，我们的目标是更好地了解人类视觉系统在判断驾驶视频质量时使用的信号和感知特征，以及如何通过计算对这些方面进行建模。我们提出了2022年的两个主要目标：（1）创建驾驶视频数据库，（2）测量可见性阈值。 这两个目标已基本实现;然而，还需要进行后续研究和收集更多的数据。对于（1），我们收集了各种环境下的驾驶视频，包括夜间视频和包含眩光的场景。我们还拍摄了户外人行道场景的视频。对于（2），我们测量了当受试者被迫注视视频中心时，添加到每个视频外围的JPEG伪影的可见性阈值（自由观看测量仍在进行中）。与这项研究相关的几个学生论文项目在2022年完成/继续。一名MS学生在驾驶视频中进行眩光检测。一个MS学生执行夜间驾驶视频的去夜。这些MS项目产生了会议出版物。BS学生项目的主题包括道路检测，检测道路上的文字，以及检测路边的电线杆/灯杆。

项目成果

Performance Evaluation of Braille Block Discriminator Using Semantic Segmentation

使用语义分割的盲文块鉴别器的性能评估

• 发表时间: 2022
• 作者: 仁田智也;平川翼;藤吉弘亘;玉木徹;長尾 海杜
• 通讯作者: 長尾 海杜